Since regression analysis is used to produce an equation that will predict a dependent variable using one or more independent variables. This equation has the form
Independent variables: compulse, dem_oth, hdi2001, pop2002
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From the above equation we would easily see that gdp1000 is predicted to increase by 3.6155 and by 16.1655 when the dem_oth and hdi2001, gdp1000 is predicted to decrease by and by 2.0032 when the variables pop2002 and Compulse goes up by one. The predicted value of gdp1000 is predicted to remain at -7.64487 if dem_oth, hdi2001, compulse, pop2002 variables are zero
We also need some measure to tell us how strongly each independent variable is associated with the dependent variable. We are trying to discover whether the coefficients on your independent variables are really different from 0 (and therefore that independent variable is ideally significant and has some effect on the dependent variable) or if any apparent differences from 0 are just due to random chance.
The null hypothesis is always that each independent variable is having absolutely no effect (has a coefficient of 0) and you are looking for a reason to reject this theory. A p-value of 5% or less is the generally accepted point at which to reject the null hypothesis.
With the p-values of compulse, dem_oth, hdi2001, less than 0.05, we reject the null hypothesis and clearly conclude that these three variables are statistically significant in the model at 95% confidence level. But with the p-value of pop2002 being greater than 0.05 we fail to reject the null hypothesis and conclude that pop2002 is not significant in the model at that level of significance.
The R-squared of the regression is the proportion of the variation in your dependent variable that is predicted by your independent variables. From the above model R2 = 67.01% therefore 67.01% of the variation in gdp1000 is explained by the independent variables, the rest may be random chances.
The overall p-value =0.0000 clearly indicates that the model is highly significant.
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_cons -7.644865 1.343332 -5.69 0.000 -10.30874 -4.980987
hdi2001 16.16546 2.433441 6.64 0.000 11.33985 20.99106
dem_oth 3.615526 1.229299 2.94 0.004 1.177781 6.053272
compulse -2.003281 .75033 -2.67 0.009 -3.491214 -.5153486
pop2002 -2.02e-09 1.78e-09 -1.13 0.259 -5.54e-09 1.51e-09
gdp_1000 Coef. Std. Err. t P>|t| [95% Conf. Interval]
gdp_1000 109 5 2.893955 0.6701 52.80402 0.0000
Equation Obs Parms RMSE "R-sq" F P
. mvreg gdp_1000 = pop2002 compulse dem_oth hdi2001
sacag85
reg_stata.docx
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